The type of machine of interest here is one that classifies objects by type and provides an estimate or count of the number of each type. If the machine is imperfect in terms of classification, then there will be a bias or error in the estimated number of objects in each category. If the machine is statistical in nature, in that the estimated number is a random variable, then there are also uncertainties due to this factor. It is possible, however, to make a standard correction such that the average error of the estimate is zero. An example is discussed for which this correction leads to a significant improvement in accuracy.
In the field of blood cell analyzer counters it is necessary to classify blood cells into about a half dozen categories or types and to count the number of cells of each category. A statistical bias occurs if there is any uncertainty in the classification process. These machines which classify objects on the bias of an analysis of certain features of that object are subject to misrecognizing a certain number of the objects. One reason for error comes from the practical problem of being able to consider only a limited number of features in the process of distinguishing objects. In this process of classifying and counting each category, the errors in classification are found to produce a bias which appears as a biased statistical error. It can be determined visually what the actual count of a sample is and compare it with the machine count to determine the bias or error of the machine. In the case of blood cell samples the actual count of cells of each blood cell type can be made manually by persons skilled at blood cell type recognition examining the slide sample under a microscope. The present invention corrects for recognition errors of a blood cell analyzer counter or of other batch processes in which various types of specimens are recognized and counted.